Exploring the epitranscriptome by native RNA sequencing

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coli and Staphylococcus aureus and extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

Quantitative profiling N1-methyladenosine (m1A) RNA methylation from Oxford nanopore direct RNA sequencing data

With the recent advanced direct RNA sequencing technique that proposed by the Oxford Nanopore Technologies, RNA modifications can be detected and profiled in a simple and straightforward manner. Majority nanopore-based modification studies were devoted to those popular types such as m6A and pseudouridine. To address current limitations on studying the crucial regulator, m1A modification, we conceived this study. We have developed an integrated computational workflow designed for the detection of m1A modifications from direct RNA sequencing data. This workflow comprises a feature extractor responsible for capturing signal characteristics (such as mean, standard deviations, and length of electric signals), a single molecule-level m1A predictor trained with features extracted from the IVT dataset using classical machine learning algorithms, a confident m1A site selector employing the binomial test to identify statistically significant m1A sites, and an m1A modification rate estimator. Our model achieved accurate molecule-level prediction (Average AUC = 0.9689) and reliable m1A site detection and quantification. To show the feasibility of our workflow, we conducted a study on in vivo transcribed human HEK293 cell line, and the results were carefully annotated and compared with other techniques (i.e., Illumina sequencing-based techniques). We believed that this tool will enabling a comprehensive understanding of the m1A modification and its functional mechanisms within cells and organisms.

Comparative analysis of 43 distinct RNA modifications by nanopore tRNA sequencing

Transfer RNAs are the fundamental adapter molecules of protein synthesis and the most abundant and heterogeneous class of noncoding RNA molecules in cells. The study of tRNA repertoires remains challenging, complicated by the presence of dozens of post transcriptional modifications. Nanopore sequencing is an emerging technology with promise for both tRNA sequencing and the detection of RNA modifications; however, such studies have been limited by the throughput and accuracy of direct RNA sequencing methods. Moreover, detection of the complete set of tRNA modifications by nanopore sequencing remains challenging. Here we show that recent updates to nanopore direct RNA sequencing chemistry (RNA004) combined with our own optimizations to tRNA sequencing protocols and analysis workflows enable high throughput coverage of tRNA molecules and characterization of nanopore signals produced by 43 distinct RNA modifications. We share best practices and protocols for nanopore sequencing of tRNA and further report successful detection of low abundance mitochondrial and viral tRNAs, providing proof of concept for use of nanopore sequencing to study tRNA populations in the context of infection and organelle biology. This work provides a roadmap to guide future efforts towards de novo detection of RNA modifications across multiple organisms using nanopore sequencing.

Co-transcriptional gene regulation in eukaryotes and prokaryotes

Many steps of RNA processing occur during transcription by RNA polymerases. Co-transcriptional activities are deemed commonplace in prokaryotes, in which the lack of membrane barriers allows mixing of all gene expression steps, from transcription to translation. In the past decade, an extraordinary level of coordination between transcription and RNA processing has emerged in eukaryotes. In this Review, we discuss recent developments in our understanding of co-transcriptional gene regulation in both eukaryotes and prokaryotes, comparing methodologies and mechanisms, and highlight striking parallels in how RNA polymerases interact with the machineries that act on nascent RNA. The development of RNA sequencing and imaging techniques that detect transient transcription and RNA processing intermediates has facilitated discoveries of transcription coordination with splicing, 3'-end cleavage and dynamic RNA folding and revealed physical contacts between processing machineries and RNA polymerases. Such studies indicate that intron retention in a given nascent transcript can prevent 3'-end cleavage and cause transcriptional readthrough, which is a hallmark of eukaryotic cellular stress responses. We also discuss how coordination between nascent RNA biogenesis and transcription drives fundamental aspects of gene expression in both prokaryotes and eukaryotes.

Elucidation of the Epitranscriptomic RNA Modification Landscape of Chikungunya Virus

The genomes of positive-sense (+) single-stranded RNA (ssRNA) viruses are believed to be subjected to a wide range of RNA modifications. In this study, we focused on the chikungunya virus (CHIKV) as a model (+) ssRNA virus to study the landscape of viral RNA modification in infected human cells. Among the 32 distinct RNA modifications analysed by mass spectrometry, inosine was found enriched in the genomic CHIKV RNA. However, orthogonal validation by Illumina RNA-seq analyses did not identify any inosine modification along the CHIKV RNA genome. Moreover, CHIKV infection did not alter the expression of ADAR1 isoforms, the enzymes that catalyse the adenosine to inosine conversion. Together, this study highlights the importance of a multidisciplinary approach to assess the presence of RNA modifications in viral RNA genomes.

Detection and Quantification of 5moU RNA Modification from Direct RNA Sequencing Data

Background

Chemically modified therapeutic mRNAs have gained momentum recently. In addition to commonly used modifications (e.g., pseudouridine), 5moU is considered a promising substitution for uridine in therapeutic mRNAs. Accurate identification of 5-methoxyuridine (5moU) would be crucial for the study and quality control of relevant in vitro-transcribed (IVT) mRNAs. However, current methods exhibit deficiencies in providing quantitative methodologies for detecting such modification. Utilizing the capabilities of Oxford nanopore direct RNA sequencing, in this study, we present NanoML-5moU, a machine-learning framework designed specifically for the read-level detection and quantification of 5moU modification for IVT data.

Materials and Methods

Nanopore direct RNA sequencing data from both 5moU-modified and unmodified control samples were collected. Subsequently, a comprehensive analysis and modeling of signal event characteristics (mean, median current intensities, standard deviations, and dwell times) were performed. Furthermore, classical machine learning algorithms, notably the Support Vector Machine (SVM), Random Forest (RF), and XGBoost were employed to discern 5moU modifications within NNUNN (where N represents A, C, U, or G) 5-mers.

Results

Notably, the signal event attributes pertaining to each constituent base of the NNUNN 5-mers, in conjunction with the utilization of the XGBoost algorithm, exhibited remarkable performance levels (with a maximum AUROC of 0.9567 in the "AGTTC" reference 5-mer dataset and a minimum AUROC of 0.8113 in the "TGTGC" reference 5-mer dataset). This accomplishment markedly exceeded the efficacy of the prevailing background error comparison model (ELIGOs AUC 0.751 for site-level prediction). The model's performance was further validated through a series of curated datasets, which featured customized modification ratios designed to emulate broader data patterns, demonstrating its general applicability in quality control of IVT mRNA vaccines. The NanoML-5moU framework is publicly available on GitHub (https://github.com/JiayiLi21/NanoML-5moU).

Conclusion

NanoML-5moU enables accurate read-level profiling of 5moU modification with nanopore direct RNA-sequencing, which is a powerful tool specialized in unveiling signal patterns in in vitro-transcribed (IVT) mRNAs.

Sequencing accuracy and systematic errors of nanopore direct RNA sequencing

BACKGROUND

Direct RNA sequencing (dRNA-seq) on the Oxford Nanopore Technologies (ONT) platforms can produce reads covering up to full-length gene transcripts, while containing decipherable information about RNA base modifications and poly-A tail lengths. Although many published studies have been expanding the potential of dRNA-seq, its sequencing accuracy and error patterns remain understudied.

RESULTS

We present the first comprehensive evaluation of sequencing accuracy and characterisation of systematic errors in dRNA-seq data from diverse organisms and synthetic in vitro transcribed RNAs. We found that for sequencing kits SQK-RNA001 and SQK-RNA002, the median read accuracy ranged from 87% to 92% across species, and deletions significantly outnumbered mismatches and insertions. Due to their high abundance in the transcriptome, heteropolymers and short homopolymers were the major contributors to the overall sequencing errors. We also observed systematic biases across all species at the levels of single nucleotides and motifs. In general, cytosine/uracil-rich regions were more likely to be erroneous than guanines and adenines. By examining raw signal data, we identified the underlying signal-level features potentially associated with the error patterns and their dependency on sequence contexts. While read quality scores can be used to approximate error rates at base and read levels, failure to detect DNA adapters may be a source of errors and data loss. By comparing distinct basecallers, we reason that some sequencing errors are attributable to signal insufficiency rather than algorithmic (basecalling) artefacts. Lastly, we generated dRNA-seq data using the latest SQK-RNA004 sequencing kit released at the end of 2023 and found that although the overall read accuracy increased, the systematic errors remain largely identical compared to the previous kits.

CONCLUSIONS

As the first systematic investigation of dRNA-seq errors, this study offers a comprehensive overview of reproducible error patterns across diverse datasets, identifies potential signal-level insufficiency, and lays the foundation for error correction methods.

The je ne sais quoi of 5-methylcytosine in messenger RNA

The potential presence of 5-methylcytosine as a sparse internal modification of mRNA was first raised in 1975, and a first map of the modification was also part of the epitranscriptomics "big bang" in 2012. Since then, the evidence for its presence in mRNA has firmed up, and initial insights have been gained into the molecular function and broader biological relevance of 5-methylcytosine when present in mRNA. Here, we summarize the status quo of the field, outline some of its current challenges, and suggest how to address them in future work.

N6-methyladenosine modification is not a general trait of viral RNA genomes

Despite the nuclear localization of the m6A machinery, the genomes of multiple exclusively-cytoplasmic RNA viruses, such as chikungunya (CHIKV) and dengue (DENV), are reported to be extensively m6A-modified. However, these findings are mostly based on m6A-Seq, an antibody-dependent technique with a high rate of false positives. Here, we address the presence of m6A in CHIKV and DENV RNAs. For this, we combine m6A-Seq and the antibody-independent SELECT and nanopore direct RNA sequencing techniques with functional, molecular, and mutagenesis studies. Following this comprehensive analysis, we find no evidence of m6A modification in CHIKV or DENV transcripts. Furthermore, depletion of key components of the host m6A machinery does not affect CHIKV or DENV infection. Moreover, CHIKV or DENV infection has no effect on the m6A machinery's localization. Our results challenge the prevailing notion that m6A modification is a general feature of cytoplasmic RNA viruses and underscore the importance of validating RNA modifications with orthogonal approaches.

Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing

N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool's hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization.

Emerging Role of Environmental Epitranscriptomics and RNA Modifications in Parkinson's Disease

Environmental risk factors and gene-environment interactions play a critical role in Parkinson's disease (PD). However, the relatively large contribution of environmental risk factors in the overwhelming majority of PD cases has been widely neglected in the field. A "PD prevention agenda" proposed in this journal laid out a set of research priorities focused on preventing PD through modification of environmental risk factors. This agenda includes a call for preclinical studies to employ new high-throughput methods for analyzing transcriptomics and epigenomics to provide a deeper understanding of the effects of exposures linked to PD. Here, we focus on epitranscriptomics as a novel area of research with the potential to add to our understanding of the interplay between genes and environmental exposures in PD. Both epigenetics and epitranscriptomics have been recognized as potential mediators of the complex relationship between genes, environment, and disease. Multiple studies have identified epigenetic alterations, such as DNA methylation, associated with PD and PD-related exposures in human studies and preclinical models. In addition, recent technological advancements have made it possible to study epitranscriptomic RNA modifications, such as RNA N6-methyladenosine (m6A), and a handful of recent studies have begun to explore epitranscriptomics in PD-relevant exposure models. Continued exploration of epitranscriptomic mechanisms in environmentally relevant PD models offers the opportunity to identify biomarkers, pre-degenerative changes that precede symptom onset, and potential mitigation strategies for disease prevention and treatment.

Unraveling C-to-U RNA editing events from direct RNA sequencing

In mammals, RNA editing events involve the conversion of adenosine (A) in inosine (I) by ADAR enzymes or the hydrolytic deamination of cytosine (C) in uracil (U) by the APOBEC family of enzymes, mostly APOBEC1. RNA editing has a plethora of biological functions, and its deregulation has been associated with various human disorders. While the large-scale detection of A-to-I is quite straightforward using the Illumina RNAseq technology, the identification of C-to-U events is a non-trivial task. This difficulty arises from the rarity of such events in eukaryotic genomes and the challenge of distinguishing them from background noise. Direct RNA sequencing by Oxford Nanopore Technology (ONT) permits the direct detection of Us on sequenced RNA reads. Surprisingly, using ONT reads from wild-type (WT) and APOBEC1-knock-out (KO) murine cell lines as well as in vitro synthesized RNA without any modification, we identified a systematic error affecting the accuracy of the Cs call, thereby leading to incorrect identifications of C-to-U events. To overcome this issue in direct RNA reads, here we introduce a novel machine learning strategy based on the isolation Forest (iForest) algorithm in which C-to-U editing events are considered as sequencing anomalies. Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. Our results suggest that iForest, known for its rapid implementation and minimal memory requirements, is a promising tool to denoise ONT reads and reliably identify RNA modifications.

The epitranscriptome of high-grade gliomas: a promising therapeutic target with implications from the tumor microenvironment to endogenous retroviruses

Glioblastoma (GBM) comprises 45.6% of all primary malignant brain cancers and is one of the most common and aggressive intracranial tumors in adults. Intratumoral heterogeneity with a wide range of proteomic, genetic, and epigenetic dysregulation contributes to treatment resistance and poor prognosis, thus demanding novel therapeutic approaches. To date, numerous clinical trials have been developed to target the proteome and epigenome of high-grade gliomas with promising results. However, studying RNA modifications, or RNA epitranscriptomics, is a new frontier within neuro-oncology. RNA epitranscriptomics was discovered in the 1970s, but in the last decade, the extent of modification of mRNA and various non-coding RNAs has emerged and been implicated in transposable element activation and many other oncogenic processes within the tumor microenvironment. This review provides background information and discusses the therapeutic potential of agents modulating epitranscriptomics in high-grade gliomas. A particular emphasis will be placed on how combination therapies that include immune agents targeting hERV-mediated viral mimicry could improve the treatment of GBM.

Genomics in the long-read sequencing era

Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.

Nanopore sequencing of internal 2'-PO4 modifications installed by RNA repair

Ligation by plant and fungal RNA ligases yields an internal 2'-phosphate group on each RNA ligation product. In budding yeast, this covalent mark occurs at the splice junction of two targets of ligation: intron-containing tRNAs and the messenger RNA HAC1 The repertoire of RNA molecules repaired by RNA ligation has not been explored due to a lack of unbiased approaches for identifying RNA ligation products. Here, we define several unique signals produced by 2'-phosphorylated RNAs during nanopore sequencing. A 2'-phosphate at the splice junction of HAC1 mRNA inhibits 5' → 3' degradation, enabling detection of decay intermediates in yeast RNA repair mutants by nanopore sequencing. During direct RNA sequencing, intact 2'-phosphorylated RNAs on HAC1 and tRNAs produce diagnostic changes in nanopore current properties and base calling features, including stalls produced as the modified RNA translocates through the nanopore motor protein. These approaches enable directed studies to identify novel RNA repair events in other contexts.

No evidence for epitranscriptomic m5C modification of SARS-CoV-2, HIV and MLV viral RNA

The addition of chemical groups to cellular RNA to modulate RNA fate and/or function is summarized under the term epitranscriptomic modification. More than 170 different modifications have been identified on cellular RNA, such as tRNA, rRNA and, to a lesser extent, on other RNA types. Recently, epitranscriptomic modification of viral RNA has received considerable attention as a possible additional mechanism regulating virus infection and replication. N6-methyladenosine (m6A) and C5-methylcytosine (m5C) have been most broadly studied in different RNA viruses. Various studies, however, reported varying results with regard to number and extent of the modification. Here we investigated the m5C methylome of SARS-CoV-2, and we reexamined reported m5C sites in HIV and MLV. Using a rigorous bisulfite-sequencing protocol and stringent data analysis, we found no evidence for the presence of m5C in these viruses. The data emphasize the necessity for optimizing experimental conditions and bioinformatic data analysis.

Emerging Quantitative Biochemical, Structural, and Biophysical Methods for Studying Ribosome and Protein-RNA Complex Assembly

Ribosome assembly is one of the most fundamental processes of gene expression and has served as a playground for investigating the molecular mechanisms of how protein-RNA complexes (RNPs) assemble. A bacterial ribosome is composed of around 50 ribosomal proteins, several of which are co-transcriptionally assembled on a ~4500-nucleotide-long pre-rRNA transcript that is further processed and modified during transcription, the entire process taking around 2 min in vivo and being assisted by dozens of assembly factors. How this complex molecular process works so efficiently to produce an active ribosome has been investigated over decades, resulting in the development of a plethora of novel approaches that can also be used to study the assembly of other RNPs in prokaryotes and eukaryotes. Here, we review biochemical, structural, and biophysical methods that have been developed and integrated to provide a detailed and quantitative understanding of the complex and intricate molecular process of bacterial ribosome assembly. We also discuss emerging, cutting-edge approaches that could be used in the future to study how transcription, rRNA processing, cellular factors, and the native cellular environment shape ribosome assembly and RNP assembly at large.

Magnipore: Prediction of differential single nucleotide changes in the Oxford Nanopore Technologies sequencing signal of SARS-CoV-2 samples

Oxford Nanopore Technologies (ONT) allows direct sequencing of ribonucleic acids (RNA) and, in addition, detection of possible RNA modifications due to deviations from the expected ONT signal. The software available so far for this purpose can only detect a small number of modifications. Alternatively, two samples can be compared for different RNA modifications. We present Magnipore, a novel tool to search for significant signal shifts between samples of Oxford Nanopore data from similar or related species. Magnipore classifies them into mutations and potential modifications. We use Magnipore to compare SARS-CoV-2 samples. Included were representatives of the early 2020s Pango lineages (n=6), samples from Pango lineages B.1.1.7 (n=2, Alpha), B.1.617.2 (n=1, Delta), and B.1.529 (n=7, Omicron). Magnipore utilizes position-wise Gaussian distribution models and a comprehensible significance threshold to find differential signals. In the case of Alpha and Delta, Magnipore identifies 55 detected mutations and 15 sites that hint at differential modifications. We predicted potential virus-variant and variant-group-specific differential modifications. Magnipore contributes to advancing RNA modification analysis in the context of viruses and virus variants.

The Baker Lab at the OHSU School of Dentistry: leveraging bioinformatics and molecular biology to discover how the bacteria that live in our mouth impact human health and disease

The microorganisms living in the human oral cavity, collectively known as the oral microbiota, play a critical role in not only oral health, but systemic and overall health. The Baker Lab leverages emerging technologies in bioinformatics and molecular biology to answer fundamental questions regarding the ecology, physiology, and pathogenesis of the oral microbiota. We use a microbial 'omics approach, which has included pioneering the use of nanopore sequencing on saliva and oral bacterial RNA. The resulting work discovered novel bacterial species and biosynthetic pathways which impact the ecology of the oral microbiota and its relationship to human disease. This article will briefly define the oral microbiota. It will also summarize how bioinformatics and 'omics-based research have revolutionized oral health research. The article will then provide a broad summary of our past, present and future research and educational programs.

Modification mapping by nanopore sequencing

Next generation sequencing (NGS) has provided biologists with an unprecedented view into biological processes and their regulation over the past 2 decades, fueling a wave of development of high throughput methods based on short read DNA and RNA sequencing. For nucleic acid modifications, NGS has been coupled with immunoprecipitation, chemical treatment, enzymatic treatment, and/or the use of reverse transcriptase enzymes with fortuitous activities to enrich for and to identify covalent modifications of RNA and DNA. However, the majority of nucleic acid modifications lack commercial monoclonal antibodies, and mapping techniques that rely on chemical or enzymatic treatments to manipulate modification signatures add additional technical complexities to library preparation. Moreover, such approaches tend to be specific to a single class of RNA or DNA modification, and generate only indirect readouts of modification status. Third generation sequencing technologies such as the commercially available "long read" platforms from Pacific Biosciences and Oxford Nanopore Technologies are an attractive alternative for high throughput detection of nucleic acid modifications. While the former can indirectly sense modified nucleotides through changes in the kinetics of reverse transcription reactions, nanopore sequencing can in principle directly detect any nucleic acid modification that produces a signal distortion as the nucleic acid passes through a nanopore sensor embedded within a charged membrane. To date, more than a dozen endogenous DNA and RNA modifications have been interrogated by nanopore sequencing, as well as a number of synthetic nucleic acid modifications used in metabolic labeling, structure probing, and other emerging applications. This review is intended to introduce the reader to nanopore sequencing and key principles underlying its use in direct detection of nucleic acid modifications in unamplified DNA or RNA samples, and outline current approaches for detecting and quantifying nucleic acid modifications by nanopore sequencing. As this technology matures, we anticipate advances in both sequencing chemistry and analysis methods will lead to rapid improvements in the identification and quantification of these epigenetic marks.